The present invention relates generally to wireless communication networks, and more particularly to techniques for obtaining and processing network parameter information in the design, implementation and/or operation of such wireless networks.
A typical wireless network includes a multitude of interconnected base stations providing wireless traffic to a varying number of fixed or mobile users distributed over a geographically well-defined coverage area. The wireless interface generally has to operate under conditions including demand for multiple access to the network, uncontrollable signal propagation, and a limited bandwidth. The demand for multiple access to the network means that location and time of service requests are not known a priori. Therefore, the network has to provide the required level of service with sufficient capacity over a large geographical area. The above-noted uncontrollable signal propagation condition indicates that a wireless link between a base station and a user relies on signal propagation in an environment that is typically associated with high propagation loss, and reflection, diffraction, or scattering effects at clutter, terrain, and other types of obstacles.
These propagation effects further lead to interference among wireless communication channels. The interference increases with the amount of traffic carried by the network, and can result in reduced quality of service, service interruption, or service discontinuation, e.g. dropped calls. This is highly undesirable and sets an upper limit to the traffic than can be carried by the network. These limitations strongly depend on-the local propagation environment, the network layout and configuration, and the spatial traffic distribution.
In order to obtain the best performance from a wireless network in terms of quality of service and amount of traffic that can be carried, network modeling tools are often utilized. Such network modeling tools include, e.g., commercially available tools such as the Planet tool from Mobile Systems International, http;//www.rmrdesign.com/msi, and the Asset tool, which is a network design tool that includes a frequency-planning algorithm, from Aircom, www.aircom.co.uk. These tools usually calculate the spatial distribution of a Radio Frequency (RF) link metric, e.g. RF field strength, or a measure of service quality, e.g. frame error rate, using a propagation-prediction algorithm and data about the network configuration, the traffic load, the terrain, and the communications-standard-specific link-budget parameters. Based on the results, the network configuration can be tuned. This approach can address a full variety of network parameters, such as, e.g., number of cells, number of communication channels per cell, antenna locations, pattern, tilt, orientation, transmit power level per communication channel and cell, frequency plan, handoff thresholds, etc. Conventional network modeling tools may be used in a design stage, when a network is upgraded, or when a network has to be readjusted to respond to changes in environment or traffic pattern.
Besides providing information about the spatial distribution of link performance, it is of value to predict an overall network performance measure as, e.g., network coverage or overall network blocking rate. Such measures help to quantify the overall performance of a network in absolute terms and improvements made when the network configuration is changed. Defining and predicting such overall network-performance measures is also necessary if optimization algorithms are used to improve a network.
The above-noted conventional network modeling tools suffer from a number of significant drawbacks. For example, their accuracy in predicting local link performance is limited by the finite resolution of terrain and clutter data and the coarse approximations of the propagation prediction algorithm. In addition, these conventional network modeling tools are typically very unreliable in predicting overall network measures, and therefore generally cannot be used as the basis for mathematical or numerical optimization processes. This is due to the fact that the prediction of a network performance measure such as network coverage can produce reliable results only if the local traffic distribution is captured in the modeling process. For example, areas with high traffic should have more weight in the network coverage analysis that those with little or no traffic.
Similar problems arise in using the conventional tools to predict a measure of network capacity or network blocking rate. For example, interference generated by a traffic hot-spot highly depends on its exact location. This issue becomes even more important in modern networks which include power control features. Small local variations in traffic distribution may lead to strong variations in the associated propagation loss. This results in incorrect power level estimations of the involved communication channels, which impacts the prediction of interference and power budget, both reflected in the effective network capacity.
Another problem is that conventional network modeling tools usually analyze a local link-performance parameter over a regular topological grid. Since such a grid does not reflect the actually existing traffic pattern, these tools fall short in providing a representative picture of overall network performance measures. Further, the discrete nature of the grid does not allow a mathematically solid definition for derivatives which is necessary if such tools are to be used as the basis for a derivative-based optimization procedure. Although numerical methods can be used instead, such methods generally require a very fine grid spacing, leading to unacceptably long processing times.
It is therefore apparent that a need exists for improved techniques for evaluation and interpolation of network parameters, for use in modeling of wireless networks, so as to overcome the above-described problems of the conventional techniques.
The present invention provides improved techniques for evaluation and/or interpolation of network parameters for use in modeling wireless networks, and may be implemented in, e.g., a processor-based system for characterizing, adjusting or optimizing the overall performance of a wireless network. In an illustrative embodiment, values of one or more link parameters of the wireless network are evaluated over test points that are derived at least in part from road location data, e.g., road maps, characterizing an area serviced by the wireless network. The evaluation may be performed using a link model. In another embodiment, values of one or more link parameters of the wireless network are interpolated along a plurality of edges between data points defining a mesh, wherein the edges correspond to roads. A measure of network performance is then generated using the interpolated values. The edges of the mesh have associated therewith a set of edge weights representative of traffic in the wireless network. The edge weights may be adjusted so as to be in agreement with available network traffic data. The mesh may be generated from, e.g., a road map file or an image file, and mesh simplification operations such as edge collapsing may be applied thereto prior to interpolation.
Examples of link parameters that may be interpolated along the edges of the mesh include the signal level of a communication channel, the signal-to-interference ratio of a communication channel, and the path loss between a network user and a base station. The network performance measure may be, e.g., a network coverage measure, such as a fraction of a target coverage area having access to a base station pilot signal at a signal-to-interference ratio above a specified threshold.
Advantageously, interpolating between the data points of a road-based mesh in accordance with the invention averages out statistical variations and permits the network performance measure to be computed as a smooth, differentiable function of the one or more link parameters, thereby simplifying network characterization, adjustment and optimization. Furthermore, the road-based interpolation of the present invention can give substantially better results in wireless network design, adjustment and optimization than the above-noted conventional topological grid.
The present invention may be implemented in one or more software programs running on a personal computer, workstation, microcomputer, mainframe computer or any other type of processor-based information processing device. These and other features and advantages of the present invention will become more apparent from the accompanying drawings and the following detailed description.